Metadata-Version: 1.2
Name: dsawl
Version: 0.1.1
Summary: A set of tools for machine learning
Home-page: https://github.com/Nikolay-Lysenko/dsawl
Author: Nikolay Lysenko
Author-email: nikolay.lysenko.1992@gmail.com
License: MIT
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        # dsawl
        
        ## What is it?
        
        This is a set of tools for machine learning. Provided by the package utilities are described in the below table:
        
        Subject | Description | Docs
        :-----: | :---------: | :--:
        Active Learning | Highly-modular system that recommends which previously unlabelled examples should be labelled in order to increase model quality quickly and significantly. Special features: various options for both exploitation and exploration. | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/active_learning_demo.ipynb)
        Stacking | A method that applies machine learning algorithm to out-of-fold predictions or transformations made by other machine learning models. Special features: support of any `sklearn`-compatible estimators (in particular, pipelines). | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/stacking_demo.ipynb)
        Target Encoding | An alternative to one-hot encoding and hashing trick that attempts to have both memory efficiency and incorporation of all useful information from initial features. Special features: `sklearn`-compatible wrapper that can transform data out-of-fold and apply an estimator to the result.| [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/target_encoding_demo.ipynb)
        
        Repository name is a combination of three words: DS, saw, and awl. DS is as an abbreviation for Data Science and the latter two words represent useful tools.
        
        
        ## How to install the package?
        
        The package is compatible with Python 3.5 or newer. A virtual environment where it is guaranteed that the package works can be created based on [the file](https://github.com/Nikolay-Lysenko/dsawl/blob/master/requirements.txt) named `requirements.txt`.
        
        To install a stable release of the package, run this command:
        ```
        pip install dsawl
        ```
        
        To install the latest version from sources, execute this from your terminal:
        ```
        cd path/to/your/destination
        git clone https://github.com/Nikolay-Lysenko/dsawl
        cd dsawl
        pip install -e .
        ```
        
        If you have any troubles with installation, your questions are welcome. 
        
Keywords: active_learning categorical_features feature_engineering
Platform: UNKNOWN
Requires-Python: >=3.5
